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24/06/2026 9:10 pm
TL;DR: Data professionals report that 44% lack access to the right data, 41% of executives rate their data as substandard, and 33% prioritise timely delivery, according to Collibra. Treating data as a product shifts the problem from raw asset management to governed reuse, which is now central to AI readiness and operational trust.
NHIMG editorial — based on content published by Collibra: Getting started with data products, a practical introduction
By the numbers:
- 44% of data professionals report not having access to the right data needed for their jobs.
- 41% of executives consider their data to be substandard.
- 33% of executives identify timely data delivery as a top priority for data operations improvement.
Questions worth separating out
Q: How should organisations govern access to data products?
A: Start by treating each data product as a governed service with an owner, an access path, and explicit usage conditions.
Q: When do data products improve governance rather than add complexity?
A: They help when the organisation can define ownership, quality expectations, and lifecycle rules consistently.
Q: What do teams get wrong about data marketplaces?
A: They often focus on discovery and ignore accountability.
Practitioner guidance
- Map data products to named owners and lifecycle states Assign an accountable owner, a support boundary, and a deprecation trigger for every published data product so consumers know who answers for change, quality, and retirement.
- Tie access requests to product contracts Require availability, refresh frequency, and usage conditions to be documented before a product is made discoverable in a marketplace or request workflow.
- Review consumption rights on a fixed cadence Re-certify who can consume high-value data products, especially where business-critical reporting, AI training, or third-party sharing is involved.
What's in the full article
Collibra's full blog post covers the operational detail this post intentionally leaves for the source:
- Role-by-role guidance for executive sponsors, program managers, data product owners, and stewards
- Workflow detail for certifying, publishing, and requesting data products inside a governed marketplace
- Platform-specific capabilities for data quality, observability, lineage, and usage analytics
- Examples of how Collibra frames federated governance across data product teams
👉 Read Collibra's practical guide to getting started with data products →
Data products and governance: what IAM teams should take from this?
Explore further
25/06/2026 6:14 am
Data products are an access governance problem as much as a data management problem. The article correctly moves beyond inventory thinking and into controlled consumption, which is where most organisations actually fail. Once data is reused across teams, the question becomes who can access it, under what conditions, and with what accountability. That places data products squarely in the same governance family as IAM and lifecycle control, not just analytics tooling. Practitioner conclusion: treat data products as governed identities for data consumption.
A few things that frame the scale:
- 90% of IT leaders say properly managing NHIs is essential for a successful zero-trust implementation, according to Ultimate Guide to NHIs.
- Only 5.7% of organisations have full visibility into their service accounts, which shows how weak entitlement visibility still is in many identity programmes.
A question worth separating out:
Q: How do data products support AI readiness in practice?
A: They make inputs more reliable by attaching business context, lineage, and quality signals to reusable data assets. That reduces ambiguity for analytics and AI systems, but only if the underlying governance model keeps those signals current as products change.
👉 Read our full editorial: Data products are becoming the control plane for trusted AI-ready data
